A VALIDATION OF THE DESIGN THINKING MINDSET MEASUREMENT MODEL AMONG SCIENCE SCHOOL STUDENTS
DOI:
https://doi.org/10.55003/JIE.25113Keywords:
Design thinking mindset, Measurement model, Science school studentsAbstract
The development of students’ Design Thinking Mindset (DTM) has become increasingly critical due to its capacity to enhance creative thinking, systematic problem-solving, and understanding of user requirements, which are essential skills in the twenty-first century. Science schools in Thailand aim to cultivate DTM in order to develop thinkers, designers, and innovators who are capable of creating social value. However, empirical studies examining the construct validity of DTM in the context of Thai science schools remain limited. This study therefore aimed to investigate the construct validity of the Design Thinking Mindset (DTM) measurement model among science school students. The participants consisted of 752 upper-secondary students enrolled in science schools. The research instrument was the Design Thinking Mindset (DTM) scale, which demonstrated strong content validity (IOC ≥ 0.80). Data were analyzed using confirmatory factor analysis (CFA). The results indicated that the DTM measurement model showed a good fit with the empirical data (χ²/df = 2.014, CFI = 0.956, TLI = 0.948, RMSEA = 0.044, SRMR = 0.035). All standardized factor loadings ranged from 0.48 to 0.87 (p < .001), supporting a high level of construct validity. In addition, all dimensions achieved acceptable values of average variance extracted (AVE > 0.50) and composite reliability (CR > 0.70), indicating strong reliability and internal consistency. Discriminant validity was supported by the Fornell–Larcker criterion for 35 out of 45 dimension pairs (77.8%). The remaining highly correlated pairs, such as Holistic Thinking–Abduction and Empathy–Abduction, reflected the integrative nature of design thinking rather than statistical redundancy. Furthermore, all Heterotrait–Monotrait Ratio (HTMT) values were below 0.85, confirming clear structural distinctiveness among the dimensions. In terms of DTM levels, students obtained the highest mean scores in Creative confidence, Holistic thinking, and Empathy, while Critical questioning and Uncertainty & Risk tolerance were at moderate levels. Overall, students’ DTM levels ranged from moderate to high (M = −0.01 to 0.03; SD = 0.92–0.96), indicating consistent development across dimensions. These findings provide empirical evidence supporting the validity and reliability of the DTM scale and support its application as a foundation for designing integrated Design thinking–STEM–BCG learning experiences that foster innovation competency and sustainable learning.
References
Best, J. W., & Kahn, J. V. (2006). Research in education (10th ed.). Pearson Education.
Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84–92.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage Publications.
Dosi, C., Rosati, F., & Vignoli, M. (2018). Measuring design thinking mindset. In T. Taura & Y. Nagai (Eds.), DS 92: Proceedings of the DESIGN 2018 15th International Design Conference (pp. 1991–2002). Zadar, Croatia: The Design Society.
Faikhamta, C., & Clarke, A. (2019). Thai cooperating teachers’ motivations and challenges in supervising student teachers during their internship program. Kasetsart Journal of Social Sciences, 40(3), 567–573. https://doi.org/10.1016/j.kjss.2017.12.018.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Education.
Henriksen, D., Richardson, C., & Mehta, R. (2017). Design thinking: A creative approach to educational problems of practice. Thinking Skills and Creativity, 26, 140–153.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (3rd ed.). McGraw-Hill.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Ladachart, L., Cholsin, J., Kwanpet, S., Teerapanpong, R., Dessi, A., Phuangsuwan, L., & Phothong, W. (2022). Ninth-grade students’ perceptions on the design-thinking mindset in the context of reverse engineering. International Journal of Technology and Design Education, 32(6), 2445–2465.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1–55.
Liedtka, J. (2018). Why design thinking works. Harvard Business Review, 96(5), 72–79.
Ministry of Education. (2021). National curriculum framework for innovation and competency-based education, B.E. 2564 (2021). Ministry of Education. (in Thai)
Office of the National Economic and Social Development Council. (2022). The 13th National Economic and Social Development Plan, B.E. 2566–2570 (2023–2027). Office of the National Economic and Social Development Council. (in Thai)
Rauth, I., Köppen, E., Jobst, B., & Meinel, C. (2010). Design thinking: An educational model toward creative confidence. In T. Taura & Y. Nagai (Eds.), DS 66-2: Proceedings of the 1st International Conference on Design Creativity (ICDC 2010) (pp. 1–8). Kobe, Japan: The Design Society.
Razzouk, R., & Shute, V. (2012). What is design thinking and why is it important? Review of Educational Research, 82(3), 330–348. https://doi.org/10.3102/0034654312457429.
Ricardo, R. (2024). Educational innovation through design thinking: Lessons from global case studies. Cambridge University Press.
Schweitzer, J., Groeger, L., & Sobel, L. (2016). The design thinking mindset: An assessment of what we know and what we see in practice. Journal of Design, Business & Society, 2(1), 71–94. https://doi.org/10.1386/dbs.2.1.71_1.
Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. https://doi.org/10.1007/BF02291170.
Vignoli, M., Dosi, C., & Balboni, B. (2023). Design thinking mindset: Measurement, structure, and implications in education and organizations. Springer Nature.
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